ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing
2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)(2023)
摘要
Unsupervised Domain Adaptation (UDA) for object detection aims to adapt a model trained on a source domain to detect instances from a new target domain for which annotations are not available. Different from traditional approaches, we propose ConfMix, the first method that introduces a sample mixing strategy based on region-level detection confidence for adaptive object detector learning. We mix the local region of the target sample that corresponds to the most confident pseudo detections with a source image, and apply an additional consistency loss term to gradually adapt towards the target data distribution. In order to robustly define a confidence score for a region, we exploit the confidence score per pseudo detection that accounts for both the detector-dependent confidence and the bounding box uncertainty. Moreover, we propose a novel pseudo labelling scheme that progressively filters the pseudo target detections using the confidence metric that varies from a loose to strict manner along the training. We perform extensive experiments with three datasets, achieving state-of-the-art performance in two of them and approaching the supervised target model performance in the other. Code is available at https://github.com/giuliomattolin/ConfMix.
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关键词
Algorithms: Machine learning architectures,formulations,and algorithms (including transfer),Image recognition and understanding (object detection,categorization,segmentation,scene modeling,visual reasoning)
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